Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don’t know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams.

This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don't have time to read some of the top papers? Get the overview here.

In most of the scientific researches, due to large amount of experiment data, statistical analysis is typically done by technical experts in computing and statistics. Unfortunately, these experts are not the experts of underlying research; which may cause gaps in analysis. If actual researchers are given easy to use tools and methods to handle and analyse data, it will enrich the research outcome for sure.

In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.

Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?

This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

This post proposes and outlines adversarial validation, a method for selecting training examples most similar to test examples and using them as a validation set, and provides a practical scenario for its usefulness.